biostudies-literatureUnknown8Vasilev Y<h4>Introduction</h4>Creating training and testing datasets for machine learning algorithms to measure linear dimensions of organs is a tedious task. There are no universally accepted methods for evaluating outliers or anomalies in such datasets. This can cause errors in machine learning and compromise the quality of end products. The goal of this study is to identify optimal methods for detecting organ anomalies and outliers in medical datasets designed to train and test neural networks in morphometrics.<h4>Methods</h4>A dataset was created containing linear measurements of the spleen obtained from CT scans. Labelling was performed by three radiologists. The total number of studies included in the sample was <i>N</i> = 197 patients. Using visual methods (1.5 interquartile range; heat map;Frontiers in artificial intelligence1607348https://www.ebi.ac.uk/biostudies/studies/S-EPMC12303909biostudies-literatureOutliers and anomalies in training and testing datasets for AI-powered morphometry-evidence from CT scans of the spleen.PMC12303909Pamova AArzamasov KAstapenko EKruchinkin ABobrovskaya TVladzimirskyy AOmelyanskaya OVladimir NVasilev YfalseOutliers and anomalies in training and testing datasets for AI-powered morphometry-evidence from CT scans of the spleen.<h4>Introduction</h4>Creating training and testing datasets for machine learning algorithms to measure linear dimensions of organs is a tedious task. There are no universally accepted methods for evaluating outliers or anomalies in such datasets. This can cause errors in machine learning and compromise the quality of end products. The goal of this study is to identify optimal methods for detecting organ anomalies and outliers in medical datasets designed to train and test neural networks in morphometrics.<h4>Methods</h4>A dataset was created containing linear measurements of the spleen obtained from CT scans. Labelling was performed by three radiologists. The total number of studies included in the sample was <i>N</i> = 197 patients. Using visual methods (1.5 interquartile range; heat map;2025-01-01T00:00:00Z20252026-07-15T12:28:14.749Z2025-08-27T03:11:10.983ZS-EPMC123039094073511110.3389/frai.2025.1607348